---
license: apache-2.0
language:
- en
- zh
metrics:
- accuracy
library_name: transformers
tags:
- text2sql
---


# text2sql-8b-instruct-v1


## 1. Summary
it is a natural language-to-SQL conversion model optimized specifically for Chinese and English users. It is based on the llama-3-chinese-8b-instruct-v3 model. We used the latest optimization algorithms to improve the performance of the model, especially in handling complex queries and multi-table joins.

### 1.1 characteristics

- Bilingual support: Ability to handle natural language queries in both Chinese and English languages.
- High accuracy: After a large number of tests on actual database queries, it has been proved that the SQL statements generated have high accuracy.


### 1.2 training data
Training data for the model comes from multiple sources, including:
- Open source databases (such as WikiSQL, Spider)
- Internally generated dataset covering a variety of query types and complexities
- User feedback data for continuous improvement of model performance

Training data is strictly screened and cleaned to ensure data quality and diversity.
### 1.3 test results
Test results on multiple benchmark datasets show the model exceeds other existing models in terms of accuracy and generation efficiency. For example:
- On the WikiSQL dataset, the model achieved an execution accuracy rate of 87.5%.
- On the Spider dataset, the model achieved an execution accuracy rate of 95.3%.

These results show the model has significant advantages in handling complex queries and multi-table joins.

## 2. Usage:
Please upgrade the `transformers` package to ensure it supports Llama3 models. The current version we are using is `4.41.2`.
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
import torch
model_id = "xbrain/text2sql-8b-instruct-v1"


messages = [
    {"role": "system", 
     "content": "I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.\n\"\n##Instruction:\n database contains tables such as table_name_30. Table table_name_30 has columns such as nfl_team, draft_year."},
    {"role": "user", 
     "content": "###Input:\nIn 1978 what is the NFL team?\n\n###Response:"},
]
pipe_msg = pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",)

outputs = pipe_msg(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```


## 3. Ethical Considerations

While fine-tuned for text to sql, this model inherits the ethical considerations of the base Llama 3 model. Use responsibly and implement additional safeguards as needed for your application.

## 4. Availability

The model is available through:
- [Hugging Face](https://huggingface.co/xbrain/text2sql-8b-instruct-v1)

For full details on responsible use, ethical considerations, and latest benchmarks, please refer to the [official Llama 3 documentation](https://llama.meta.com/).